Rapid diagnosis of a disease condition using infrared spectroscopy

ABSTRACT

Reliable and rapid diagnostic methods for many functional syndromes (FS) such as Bladder Pain Syndrome/Interstitial Cystitis (IC) are not available. Exemplary embodiments include rapid and accurate methods for diagnosing FS in humans and other mammals using infrared microspectroscopy (IRMS). Exemplary methods utilize Soft Independent Modeling by Class Analogy (SIMCA) to create classification models. Exemplary methods utilize classification models to categorize a subject&#39;s condition (e.g., healthy/sick and or flare/remission). Using these classification models, various embodiments enable diagnosis based on spectra data from a blood sample or other biomedical specimen. Exemplary embodiments may be useful for rapid diagnosis of IC and various other conditions in humans, cats, and/or other mammals.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority of U.S. Provisional Application No. 61/334,929, filed May 14, 2010, which is hereby incorporated by reference in its entirety. This application is also a continuation-in-part application and claims priority to U.S. patent application Ser. No. 12/933,960, filed Dec. 15, 2010, which is a national stage of entry of International Patent Application No. PCT/US2009/038771, filed Mar. 30, 2009 and now expired, which is in turn entitled to the benefit of priority of U.S. Provisional Application No. 61/040,627, filed Mar. 28, 2008 and now expired, all of which are hereby incorporated by reference in their entirety.

TECHNICAL FIELD

The present invention relates to methods for diagnosing disease conditions in a patient, particularly rapid diagnostic methods using multivariate classification models.

BACKGROUND OF THE ART

Functional syndromes (FS) are illnesses characterized by constellations of symptoms, suffering, and disability that cannot be explained by an evidence-based pathophysiologic mechanism. Functional syndromes, such as chronic fatigue syndrome, fibromyalgia, interstitial cystitis (IC), and irritable bowel syndrome are among the most common reasons human beings seek medical care. Physicians generally adopt a symptomatic empirical approach for these conditions. They are important causes of chronic illness and healthcare utilization, because patients often do not get better as a result of currently available therapies.

Patients with FS are seen by all medical specialties. IC is one of the most common FS in urology. IC is characterized by chronic pain, excessive urgency and frequency of urination, nocturia, and negative urine cultures. As many as 750,000 women in the United States may suffer from IC. Data from the Nurses' Health Study suggests that the prevalence of IC among women is about 52-67 per 100,000. Patients often reach a diagnosis of IC through an emotionally charged and difficult route, the product of numerous physician encounters and stigmatizing experiences. IC interferes with employment, social relationships and sexual activity. The chronic pain, frequency, urgency and sleep deprivation associated with IC may contribute to psychological stress and secondary depression. Chronic bladder and pelvic pain is usually moderate to severe in patients with IC; more than one half of symptomatic patients also suffer from depression, and suicidal ideation is 3-4 times more common in these patients than in the general population.

Diagnosing FS is difficult. One of the reasons for this is that there may be a variety of nonspecific symptoms associated with the FS. Before a diagnosis of IC can be made, for example, many non-related conditions and disorders (urinary tract infections, vaginal infections (in women), chronic prostatitis (in men), bladder cancer, bladder inflammation or infection, kidney stones, endometriosis, neurological disorders, sexually transmitted diseases among other) must be ruled out. Currently there is no well accepted diagnostic test for many functional syndromes like IC. It is therefore an unmet advantage of the prior art to provide a simple, rapid, and reliable method to diagnose functional disorders such as IC. A similar syndrome, called feline IC (FIC), is the most common lower urinary tract disorder of domestic cats.

SUMMARY OF THE INVENTION

This and other unmet advantages are provided by disclosed methods described and shown in more detail below.

Embodiments demonstrate that the combination of infrared microspectroscopy (IRMS) of a sample and multivariate data analysis is an ideal technology for simple, rapid, reagent-free, non-invasive, and high-throughput screening and analysis of complex biological samples. The technique uses a “training set” of samples of known origin to establish a characteristic spectral pattern, to which subsequent samples can be compared. The IRMS “fingerprint” permits identification of subtle physiological differences that permits clustering of samples into specific diagnostic groups. This technology is a reliable method for diagnosis of diseases such as IC and CPPS, and almost any other disease condition.

Various embodiments combine infrared microspectroscopy (IRMS) with the power of multivariate statistical approaches such as principal components analysis (PCA) in methods useful for rapid and reliable clinical diagnostic analyses. Exemplary embodiments utilizing multivariate classification models may be used for the rapid diagnosis of IC and other disorders from serum or other fluid biomedical specimens.

Embodiments include methods for identifying a test subject's condition, such as if a test subject's disease is in “flare” or in “remission,” by collecting infrared spectral data derived from a fluid biomedical sample. Embodied methods permit the identification of the “state” or stage of a disorder, severity of a disorder, and or the trait vulnerability for a disorder.

Exemplary embodiments provide methods for rapidly diagnosing specific disease conditions such as IC and other functional syndromes. At least one embodiment comprises a method for diagnosing a disease condition of a subject, comprising: obtaining a sample from the subject (e.g., a fluid biomedical specimen); separating the sample to obtain a desired fraction; depositing an aliquot of the desired fraction onto a slide; drying the aliquot; collecting infrared spectral data from the aliquot; and categorizing the subject's condition by analyzing the infrared spectral data using multivariate classification models.

In various embodiments, the sample may be diluted to form a sample-water solution prior to separating the specimen. In some embodiments, the separating step may include the step of filtering the specimen with a membrane. The separating step may include the step of centrifuging the specimen using a centrifugal filter device.

In an exemplary embodiment, multivariate classification models may be developed based on a principle components analysis using previously provided spectra from afflicted and non-afflicted subject populations. In an alternative embodiment, the multivariate classification models may be downloaded from a reference database.

In at least one embodiment, the drying step further includes the step of forming a dried film on the slide; and the collecting step comprises obtaining infrared spectral data from a pre-selected region of the dried film. In a preferred embodiment, at least two spectra collection regions are substantially equidistant from the center of the dried film.

Although various embodiments utilize dried films for the collection of IR spectra information, the infrared spectral data in the collecting step may alternatively be obtained via an attenuated total reflectance Fourier transform (FT)-IR method. In this case, the need for diluting the sample or the creation of dried films may be obviated.

Exemplary methods presented herein may assist the clinician by providing a rapid and reliable means for diagnosing functional syndromes like IC after receiving no more than a fluid biomedical specimen from the patient.

Additionally, various embodiments demonstrate the feasibility of using a bloodspot card for sample collection. In these embodiments, blood can be directly collected onto the card, which need not be frozen immediately, making this an ideal method to facilitate transportation and storage. If desired, the collection card, with dried sample, may be easily transported from the collection location to a testing facility. Accordingly, in some embodiments, the obtaining step may comprise the substeps of: providing a sample collection card; depositing a sample onto the collection card; and, after a drying period, resuspending the specimen in a fluid medium.

In various embodiments, the step of categorizing the subject's condition results in classifying the disease condition is in flare or remission. In some embodiments, the step of categorizing the subject's condition results in classifying the stage of the disease condition. In various embodiments, the step of categorizing a subject's condition comprises the step of determining the severity of a disease condition.

BRIEF DESCRIPTION OF THE DRAWINGS

A better understanding of the embodiments will be obtained from a reading of the following detailed description and the accompanying drawings in which:

FIG. 1 shows a dried film of an exemplary embodiment. Squares show typical regions where spectra may be collected.

FIG. 2 shows typical reflectance infrared microspectroscopy spectrum of human and domestic cat serum films collected in the 4000-700 cm⁻¹ region.

FIG. 3 shows infrared microspectroscopy spectra of cat serum sample before and after membrane filtration.

FIG. 4 shows Coomans plots of IR microspectroscopy spectra. The vertical and horizontal lines represent the 0.05 confidence limits between groups. (A) human serum samples for IC/CPPS-IIA diagnosis, demonstrating that the healthy and affected classes of test samples did not share common multivariate space and (B) domestic cat serum for FIC, healthy and subjects with chronic disorders other than FIC. (C) Cooman's plot of cat serum samples after membrane filtration, which increased the distance (difference) between the groups.

FIG. 5 shows (1) discriminating power plot of human and feline SIMCA models based on the infrared spectra of dried serum films (4-class models). The same infrared region (1500-1700 cm−1) was important for models of both species. Letters A (˜1660 cm−1) and B (˜1740 cm−1) denote similar bands. (2) Discriminating power plot of cat serum samples after membrane filtration (2-class model).

FIG. 6 shows a 3-D projection (A) and a Coomans plot (B) demonstrating successful single gender classification of 25 women with IC and 23 healthy women.

FIG. 7 shows a 3-D projection (A) and Coomans plot (B) of 14 IC and 16 healthy women demonstrating successful single gender classification using blood spot card samples.

FIG. 8 shows a 3-D projection (A) and Coomans plot (B) demonstrating successful discrimination of 6 IC patients in flare and 3 IC patients in remission.

FIG. 9 is a plot demonstrating a linear relationship of O'Leary-Sant scores with spectral information for 8 women with IC.

DETAILED DESCRIPTION

Vibrational spectroscopic methods such as Raman and FT-IR spectroscopy are emerging as powerful techniques in biomedicine. IR spectroscopy is a well established analytical technique for rapid, high-throughput, non-destructive analysis of a wide range of sample types based on the principle that chemical structures in biological samples absorb particular frequencies of infrared light. The IR absorption spectrum provides a characteristic “fingerprint” of substances such as lipids, proteins, nucleic acids, polysaccharides, and phosphate-carrying compounds present in the sample. The resulting infrared absorption spectrum provides information concerning the biochemistry of biological fluids and tissues, and has the ability to detect biochemical changes caused by pathologies, even at very early stages of the disease.

In IR microspectroscopy the IR spectrum is collected from a particular area of the sample by using an integrated microscope; this technique offers the capability to associate IR spectra with specific structures in the specimen.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the exemplary embodiments, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

The section headings used herein are for organizational purposes only and are not to be construed as limiting the described subject matter in any way. It will be appreciated that there is an implied “about” prior to metrics such as temperatures, concentrations, and times discussed in the present teachings, such that slight and insubstantial deviations are within the scope of the present teachings herein. In this application, the use of the singular includes the plural unless specifically stated otherwise. Also, the use of “comprise,” “comprises,” “comprising,” “contain,” “contains,” “containing,” “include,” “includes,” and “including” are not intended to be limiting. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. The articles “a” and “an” are used herein to refer to one or to more than one (i.e., to at least one) of the grammatical object of the article. By way of example, “an element” means one element or more than one element.

“Subjects” as described herein are generally human subjects and include medical patients. The subjects may be male or female and may be of any race or ethnicity, including but not limited to Caucasian, African-American, African, Asian, Hispanic, Indian, etc. The subjects may be of any age, including newborn, neonate, infant, child, adolescent, adult, and geriatric. Subjects may also include animal subjects, particularly mammalian subjects such as dog, cat, horse, mouse, rat, etc.

At least one exemplary embodiment includes the following steps:

-   -   1. Prepare a solution using the patient's serum and distilled         water.     -   2. Use separation techniques, such as ultrafiltration, to         fractionate serum constituents.     -   3. Prepare a dried serum film (DSF) on a microscope slide by         vacuum drying 0.5-1.0 μl of the solution fraction from step 2.     -   4. Collect IR spectra from the DSF.     -   5. Use established statistical methods to diagnose the subject         condition using the DSF IR spectral information.

In an alternative embodiment, Attenuated Total Reflectance (ATR) techniques can be used (described below) which may obviate the need to dilute the specimen or produce a film. In such a case, the fluid specimen can be deposited directly and subsequently analyzed. The sample is then brought into direct contact with a crystal (internal reflection element, IRE) that has a much higher refractive index than that of the sample. When the angle of incidence of the light from the IRE to the sample exceeds the crystal angle, total internal reflection takes place. When this total internal reflection takes place, the light penetrates slightly into the sample producing an absorbance-like spectrum.

Various embodiments include a method for rapidly diagnosing a disease condition of a subject, comprising: obtaining a sample from the subject, separating the sample to obtain a desired fraction; depositing an aliquot of the desired fraction onto a slide; drying the aliquot; collecting infrared spectral data from the aliquot; and categorizing the subject's condition by analyzing the infrared spectral data using multivariate classification models. In some embodiments, the obtaining step comprises the substeps: providing a sample collection card; depositing the sample onto the collection card; and resuspending the sample in a fluid medium.

In some embodiments, the step of categorizing the subject's condition results in classifying the disease condition as in flare. In other embodiments, the step of categorizing the subject's condition results in classifying the disease condition as in remission. In various exemplary embodiments, the step of categorizing the subject's condition results in classifying the disease condition by stage.

In an exemplary embodiment, multivariate classification models may be developed based on a principle components analysis using previously provided spectra from afflicted and non-afflicted subject populations. In an alternative embodiment, the multivariate classification models may be downloaded from a reference database.

In example embodiments, the drying step may comprise the application of a vacuum. In various embodiments, the drying step results in a dried film on the slide; and the collecting step comprises the step of obtaining infrared spectral data from at least two regions of the dried film. The collecting step may comprise obtaining infrared spectral data from at least three regions of the dried film. In some embodiments, the spectra collection regions are substantially equidistant from the center of the dried film. In specific embodiments, the spectral data includes information for wavelengths between 1300 and 1800 Cm⁻¹.

An exemplary method may comprise diluting the sample to form a sample-water solution prior to the depositing step. In various embodiments, the sample comprises a fluid biomedical specimen from the subject and the sample-water solution contains at least one part water for each part fluid specimen. In some embodiments, the aliquot comprises about 0.01 μl to 1 μl of the desired fraction.

In some embodiments, the infrared spectral data in the collecting step are obtained via an attenuated total reflectance FT-IR method.

In embodiments, the separating step includes the step of filtering the sample with a membrane. In other embodiments, the separating step may include the step of ultrafiltering the sample with a molecular weight cut-off membrane of a predetermined stringency. The separating step may include the step of centrifuging the sample using a centrifugal filter device.

EXAMPLES

The following examples demonstrate various techniques and procedures useful for the exemplary embodiments. Although embodiments disclosed herein use serum as the fluid biomedical specimen, one skilled in the art will appreciate that many different fluid biomedical specimens (e.g., urine, semen, saliva, etc) may be analyzed according to exemplary methods of the various embodiments described below. Furthermore, although embodiments demonstrated herein relate primarily to IC, the invention is not so limited. Because many disease states lead to changes in the chemical composition of biomedical fluids like serum, embodiments of the present invention may be broadly applicable for the diagnosis of many disease conditions.

Example 1 Collection of Fluid Biomedical Specimens

Cat Specimens: frozen serum samples from 34 domestic cats (Felis silvestris catus) were provided by the Department of Veterinary Clinical Sciences of The Ohio State University (OSU) and diagnosed by Dr. Tony Buffington based on a complete physical diagnosis and the pertinent NIDDK inclusion and exclusion criteria. 22 samples came from cats with FIC and 12 from healthy cats (18 female and 16 male). Samples were stored at −40° C. in the Food Science Department of the OSU.

Human Specimens: 30 frozen samples of human serum from the Harvard Urological Diseases Center (Boston, Mass.) were received and stored at −40° C. in the Food Science Department of The Ohio State University. 18 from these samples belonged to IC patients and 12 to healthy subjects; 15 were from female and 15 were from male individuals. One human sample was removed from the study because of the presence of small particles and atypical dried film formation. The IC patients satisfied the criteria for the diagnosis of IC described by the NIDDK.

Example 2 Preparation of IR-Films

Dried Serum Films (DSF)

Serum was separated by centrifugation and samples were immediately stored at −20° C. to maintain their integrity until analysis. Two types of DSF were prepared using 1:1 serum/water solutions. The first type of films (type α) was prepared with human and cat sera by vacuum drying (˜4 minutes) 0.5 μl of the serum solution over high infrared reflectance microscope slides (circular area of 3.8 mm²) The second type of DSF (β) was prepared by ultrafiltering the serum solution through a cellulose membrane of 10,000 nominal molecular weight limit (NMWL) at 16,000×g for 10 min using a microcentrifuge (Model 5415R, Eppendorf, Westbury, N.Y.).

For α and β DSF, an aliquot (0.5-1 μl) of the filtrate was placed onto SpectCONC-IR or SpectRIM (Tienta Sciences, IN) microscope slides (circular area of 3.8 mm²) and dried at room temperature under vacuum to produce dried serum films (DSF). (DSF type β was evaluated only with cat samples). IR spectra collected from the DSF were used to create classification models using Soft Independent Modeling by Class Analogy (SIMCA) (details below).

The preparation of DSF was found to impact the reproducibility of the IR measurements. FIG. 1 shows a microscope picture of a cat DSF (CDSF). Three regions were selected for spectra collection. (FIG. 1). Three main factors influenced the structure of DSFs: the concentration of the serum solution, the volume of sample to dry (given a specific drying area) and the drying time. Although a much wider range of concentrations may be appropriate for any given application, a 1:1 to 1:5 solution of serum in distillated water yielded acceptable IR spectra data for these examples.

These solutions allowed for low sample volumes per film (0.5-1.0 μl), high infrared absorbance, and short drying times (˜4 min) For a given application, the aliquot may comprise as little as 0.01 μl to 1 ml of the desired fraction. Collecting spectra from DSF areas with similar appearance, roughly equidistant to the center of the film, increased the reproducibility of the protocol.

Example 3 IR Spectra Collection

IR spectra were collected using an FT-IR microscope (UMA 600 series IR microscope interfaced with a FTS Excalibur 3500 GX FTIR spectrometer; Varian, Calif.) equipped with a broadband mercury-cadmium-telluride detector. Each spectrum was obtained from a 0.0625 mm² square area with a frequency range of 4000 to 700 cm⁻¹ by co-adding 128 scans at a resolution of 8 cm⁻¹. Immediately after vacuum drying the serum solution 3 spectra from different regions roughly equidistant to the center were collected from every film. Two films were prepared from each serum solution; thus, a total of 6 spectra were gathered per sample.

FIG. 2 shows typical reflectance infrared microspectroscopy spectrum of human and domestic cat serum films collected in the 4000-700 cm⁻¹ region. Infrared spectra from feline (FSF) and human serum films (HSF) showed similar spectral profiles (FIG. 2). Although no observable differences between spectra from (F)IC affected and healthy subjects (not shown) were apparent, dissimilarities were detected after mathematical transformations (normalization, second derivative) and multivariate analysis.

Most serum protein (60-80 mg/mL) are large (65-97% is serum albumin and immunoglobulins); however, biomarkers often are low-molecular-weight proteins or peptides secreted into the bloodstream as a result of a disease process. To reduce interferences caused by large molecules that may not be related to IC, films were prepared after ultrafiltration of cat serum solutions using a 10,000 molecular weight cut-off membrane (DSF β). Differences in IR spectral features between films prepared with or without the ultrafiltration step were evident (FIG. 3), with a marked decrease in some IR absorption bands in the 1400-1800 cm⁻¹ region, probably due to the removal of large proteins.

As a prophetic alternative to collecting spectra data from dried films, various embodiments may employ Attenuated Total Reflectance (ATR) techniques to obtain the spectra information. See e.g., United States Patent Application 20080285122, hereby incorporated by reference in its entirety. ATR is a technique used in spectroscopy, such as FT-IR spectroscopy, to obtain spectral measurements from samples that are difficult to analyze by other means such as transmission or reflection.

This technique relies on total internal reflection. The sample is placed proximately into direct contact with a crystal (known as an internal reflection element, IRE). The crystal has a refractive index that is much higher than that of the sample. When the angle of incidence of the light from the IRE to the sample exceeds the crystal angle, total internal reflection takes place. When this total internal reflection occurs, the light penetrates slightly into the sample producing an absorbance-like spectrum.

Typically apparatus for carrying out ATR measurements will comprise a spectrometer to provide wavelength discrimination, an illumination system for directing light onto a sample, an ATR optic which provides a sample plane and a collecting/detecting system to capture light that has interacted with the sample.

The ATR optic is arranged in such a way as to reflect all incident light from a designated sample plane by means of the phenomenon of total internal reflection. Spectral information concerning the sample is derived from the interaction of the sample with an evanescent electric field that exists immediately outside the reflecting surface. The absorption of energy from this field attenuates the reflection and impresses spectral information on the light beam.

An imaging ATR system can be constructed based upon these principles by arranging to illuminate an area of a sample and by arranging the collecting system to have imaging properties. Light returning from spatially distinct regions of the sample is collected on a detector or a detector array such as a one dimensional or two dimensional array of detectors; spectral information thus collected can be compiled into a spectral image of sample.

An imaging ATR system can be constructed in the form of a reflectance microscope. In such an arrangement, light is directed onto and collected from a reflective sample by means of an imaging optic. An ATR optic for such a system can conveniently comprise a hemispherical plano-convex lens made of a high refractive index material such as germanium. The optic is arranged so that the convex spherical surface is directed towards the microscope optic with its centre of curvature arranged to be coincident with the focal plane of the imaging system. The sample is presented to the flat surface of the ATR.

The microscope includes a moveable stage which has associated motors for moving the stage in x, y and z directions under processor control. Imaging is carried out using a small linear array detector and physically moving the stage and therefore the crystal/sample combination laterally relative to the optical axis of the microscope. As the stage is moved images can be collected by the detector from different parts of the sample and in this way a spatial image can be accumulated.

ATR generally allows qualitative or quantitative analysis of samples with little or no sample preparation, which greatly speeds sample analysis. The main benefit of ATR sampling comes from the very thin sampling path length and depth of penetration of the IR beam into the sample. This is in contrast to traditional FT-IR sampling by transmission where the sample usually is diluted with an IR transparent salt and pressed into a pellet or a thin film prior to analysis to prevent totally absorbing bands in the IR spectrum.

Example 4 Creation of Classification Models

Classification Models

Spectral differences between samples from subjects with (F)IC, subjects with other medical disorders, and asymptomatic controls were evaluated using multivariate statistical techniques to resolve spectral information of interest and to cluster the samples according to the presence of the disease. Soft Independent Modeling Class Analogy (SIMCA) was carried out using Pirouette pattern recognition software (V 3.02 for Windows NT, Infometrix, Inc., Woodinville, Wash.). Sample residual and Mahalanobis distance were used for outlier diagnostics. The scores plot was used for visualization of clustering among samples (sample patterns, groupings or outliers).

Coomans' plots were applied (FIG. 4) to assess the classification performance of the SIMCA model by predicting class membership in terms of distance from the boundaries of the prediction model. In a Coomans' plot, the two axes represent the distance of each sample from a specific class (e.g., IC or healthy), so that each class model is drawn as a rectangle corresponding to the critical distance (p=0.05) from the class. Any sample having a distance from the corresponding class rectangle greater than the critical distance is identified as being outside the class model and, as a consequence, rejected as an outlier from the specific class (graphically, it is plotted outside the rectangle defining the class model). Samples plotted onto the lower left square of the diagram are assigned to both classes.

Initial IR spectra from feline (FSF) and human serum films (HSF) showed similar spectral profiles (FIG. 1) with no observable differences between spectra from (F)IC affected and healthy subjects. However, SIMCA analysis of the transformed (second derivative) IR spectra revealed specific bands that permitted the classification of samples according to presence or absence of (F)IC (FIG. 4A), with interclass distances of 4.1 and 7.9 for cats and humans (see Table 1), respectively, providing evidence of chemical differences between the serum of healthy and (F)IC afflicted subjects. Spectra from cats suffering disorders other than (F)IC differed from the healthy control and (F)IC groups (FIG. 4B).

TABLE 1 Interclass distances in 2-class SIMCA models using type α DSF. Human Model Cat Model Healthy (16) Healthy (21) Sick (35) 7.9 Sick (33) 4.1 *Number of factors between parentheses **F-Test confidence level: 95%

The fundamental role of interclass distance in multivariate classification follows from the assumption that proximity in multivariate space is indicative of similarity between samples. Samples that are nearby in the multivariate space are considered to have similar characteristics, whereas large separation suggests different characteristics. In general, class distances greater than 3 suggest well separated classes.

The ability to classify the subjects into these two groups denotes spectral differences between the two classes, and provides evidence of chemical differences between the serum of healthy and IC-afflicted subjects. Remarkably, these chemical differences could be detected in films containing all serum constituents in both human and cat samples (DSF type α).

The classification of specimens as (F)IC or healthy using type α DSF produced complex SIMCA models (large number of factors) presumably due to the presence of many other compounds present in the samples not related with the disease (hormones, lipids, etc). To decrease the complexity of the models and increase the prediction capabilities various embodiments employ two strategies by: (a) introducing more information (classes) in the models (subject gender) and (b) extracting serum constituents that may not be related with the disease (type β DSF).

Regarding (a), the inclusion of gender in the classification models (4-class models: healthy-male, healthy-female, sick-male, and sick-female) produced less complex models with fewer numbers of factors in each class and larger interclass distances (Table 2).

TABLE 2 Interclass distances in 4-class SIMCA models using type_DSF. Healthy Male IC Male Healthy Female Human Healthy Male (10) — — — IC Male (11) 19.5 — — Healthy Female (9) 11.6 6.2 — IC Female (14) 12.6 3.9 7.1 Cat Healthy Male (8) — — — IC Male (15)  7.1 — — Healthy Female (12) 16.8 5.7 — IC Female (14) 17.1 5.8 4.9 *Number of factors between parentheses **F-Test confidence level: 95%

These results corroborate that IR spectra contained information about other characteristics of the subjects that could be included in the models to simplify and increase their prediction capabilities. Besides gender, other characteristics, such as age, diet, intercurrent disease, that might influence serum composition could be introduced as factors to make SIMCA models more robust.

Regarding (b), SIMCA models generated using the IR spectra from membrane-filtered cat serum solutions (FIG. 4C) resulted in greater interclass distances between FIC and healthy subjects, from 4.1 to 5.9, and again correctly classified each one of the spectra.

Example 5 Diagnosis Capabilities

Prediction Models.

The diagnostic capabilities of the method were evaluated using type α DSF: 4 spectra from every sample were put together to create SIMCA prediction models for cats (136 spectra) and for humans (116 spectra). The other 2 spectra from every sample (68 from cats and 58 from humans) were used as blinds to evaluate the models as percentage of blind spectra correctly classified. To assess the variability introduced by film preparation new DSF were made from random samples collected from 10 cats. At least 2 of the 3 spectra from every new film had to be recognized by the SIMCA models to consider the film correctly identified. Whenever spectra from the same film had contradictory identifications, the results were reported as misidentified.

SIMCA prediction models based just on the condition of the subjects (2-class) were able to identify 96% (55/57) and 93% (63/68) of human and cat blind spectra respectively (Table 3). 4-class prediction models performed better in the identification of blind spectra, correctly identifying 98% (56/57) of human and 97% (66/68) of cat specimens.

TABLE 3 Identification of blind spectra using SIMCA prediction models Human Models Feline Models Healthy-Male, Healthy-Male, Sick-Male Sick-Male Healthy-Female, Healthy-Female, SIMCA Classes Healthy or Sick Sick-Female Healthy or Sick Sick-Female Number of specimens 29 29 34 34 Total collected spectra 174 174 204 204 Spectra used for SIMCA models 116 116 136 136 Spectra used as blinds* 57 57 68 68 Correcly classify 55 56 63 66 Mispredicted 0 0 0 0 Not-recognized 2 1 5 2 Correcly diagnose specimes** 100% 100% 100% 100% *One HDSF spectrum considered anomalous was removed from the study. **At least 1 of the 2 blind spectra from each specimen was recognized. There were no mispredictions.

These results support the feasibility of detecting specific information in the DSF infrared spectra able to determine if a subject is afflicted with (F)IC or not. 100% of the subjects were correctly diagnosed by predicting their condition using the blind spectra. For this evaluation, specimens were considered to be correctly diagnosed if at least 1 of the 2 blind spectra was correctly classified, and there was no contradiction between the two.

The spectra used as blinds in the previously mentioned evaluation came from the same group of DSF produced to make the SIMCA models and therefore did not consider the variability introduced by the dried film preparation procedure. To further evaluate the method we tested the models with IR spectra from a new set of DSF (Table 4). Sixteen of 20 new dried films prepared to evaluate the variability produced by the DSF preparation step were correctly identified (at least 2 out of 3 spectra had to be identified per film). This shows that film preparation, indeed, increased the variability and reduced the prediction capabilities of the models. Yet, the spectra collected from these new dried films were enough to correctly identify the condition of all specimens involved in the test (at least 1 DSF per subject and no misidentifications).

TABLE 4 Identification of blind spectra from new CSDF. New films (3 Correctly spectra/ identified Not- Wrongly Specimen Class film) Films* recognized identified A female-sick 2 2 0 0 B male-sick 2 2 0 0 C female-sick 2 1 1 0 D female-sick 2 2 0 0 E female-healthy 2 1 1 0 F female-healthy 2 2 0 0 G male-healthy 2 2 0 0 H male-sick 2 1 1 0 I female-healthy 2 2 0 0 J male heathy 2 1 1 0 TOTAL 20 16 4 0 *At least 2 of the 3 spectra from every film had to be correctly classified for the film to be considered identified.

In a SIMCA discrimination power graph values close to 0 indicate low discrimination ability of a variable (in IR spectroscopy each wavelength is considered a variable), whereas larger values imply high discrimination power. Notably, the discrimination power analysis of the 4-class models revealed that infrared wavelengths between 1300 and 1800 cm−1 were key for the classification of human and feline subjects (FIG. 5), and suggest chemical similarities in the IC expression for both species.

Example 6 Quantitative Technique to Identify Compounds Responsible for the Spectral Differences

The SIMCA models revealed that most of the variance between (F)IC and healthy subjects could be explained by changes in IR peaks in the 1550-1600 cm¹ range, suggesting chemical similarities of the biomarker in both species (FIG. 5-1). As shown in FIG. 5-2, the band at 1597 cm⁻¹, associated with indole ring vibrations, was one of the most important signals to discriminate healthy cats from cats with FIC, suggesting that derivatives from tryptophan may be linked to functional syndromes like (F)IC.

Based on the information provided by IRMS, we developed a liquid chromatography-mass spectrometric (LC-MS) method to measure concentrations of tryptophan and two of its metabolites, kynurenine and 5-hydroxy indole acetic acid, in cat serum.

Analytes were separated using reversed-phase liquid chromatography on a 3.5 μm Symmetry C18 column (4.6×150 mm, Waters Corp., Milford, Mass., USA). The mobile phase (0.8 ml/min) was acidified water (0.5% TFA) during the first 8 minutes; afterwards, methanol was linearly introduced from 0 to 30% in 12 minutes. Spectral information over the wavelength range of 254-700 nm was collected on a PDA detector. Mass spectrometry was conducted on a quadrupole ion-tunnel MS equipped with electrospray ionization (ESI) interface (Shimadzu, Columbia, Md.). Biomarker identification was done with authentic standards.

The 10 FIC cats examined had higher serum concentrations of tryptophan (21% higher; % RSD: 23% (affected), 19% (healthy); p=0.07) and kynurenine (23% higher; % RSD: 27% (affected), 23% (healthy); p=0.09) than did the 10 healthy cats, whereas concentrations of 5-hydroxy indole acetic acid were not different (p=0.45). Interestingly, sera from patients with chronic fatigue syndrome reportedly also have comparably higher concentrations of tryptophan than does sera from healthy individuals.

Example 7 Collection of Sample onto Blood Spot Card

In various embodiments, bloodspot cards may be used for sample collection. In these embodiments, blood can be directly collected onto the card, which need not be frozen immediately, making this an ideal method to facilitate transportation and storage. If desired, the collection card, with dried sample, may be easily transported from the collection location to a testing facility.

The following represent acceptable techniques for collecting and analyzing a fluid biomedical specimen using a blood spot card. One of ordinary skill in the art will appreciate there are numerous methods for obtaining a sample (e.g., a fluid biomedical specimen) from a subject.

Deposition on Collection Card

An example technique may include finger puncture, which may comprise:

-   -   1. Select skin puncture site. One may use either hand but the         less dominant hand is usually not as callused. Use the palmar         surface of the distal phalanx of the middle or ring finger (3rd         or 4th digit). The only finger that should not be used is the         last finger (5th digit).     -   2. Warm the site if necessary.     -   3. Organize equipment.     -   4. Clean the site with 70% isopropyl alcohol. Allow the site to         dry completely.     -   5. Puncture the site with a disposable lancet. Hold the finger         and hand firmly to immobilize the finger as some patients'         response is to pull away as the skin puncture is performed. This         often necessitates repeating the procedure in order to obtain         the sample. There are two basic types of lancets. One type has         an exposed blade that will penetrate the skin to a         pre-determined depth. When using this type of lancet, perform a         quick puncture to the pre-determined depth of the lancet,         perpendicular to the fingerprints and at a 90 degree angle. The         second type is a semi-automatic lancet. This type has either a         plunger or button to push to perform a puncture or an incision.         To use these, again immobilize the finger and hold the lancet at         a 90 degree angle to the finger, perpendicular to the lines of         the fingerprint. Using moderate pressure, depress the plunger         completely, then release the plunger and remove the lancet.     -   6. Wipe away the first drop of blood using clean gauze. This         drop of blood contains the highest concentration of tissue         fluids.     -   7. Collect the specimen by allowing the drops of blood to fill         circles printed on the bloodspot card. If the blood flow becomes         inadequate or stops, repeat the skin puncture using a different         site and a new lancet.     -   8. Discard the lancet in a puncture resistant biohazard sharps         container.     -   9. Apply pressure to the puncture site using sterile gauze.     -   10. Apply a band-aid, if necessary.

Sample Preparation

An acceptable technique for preparing the sample from the blood spot card includes the following:

-   -   1. Cut the blood sample from the blood spot card using the punch         and place it in a 1.5 milliliter centrifuge tube.     -   2. Add 150 microliters of methanol to the centrifuge tube         containing the sample.     -   3. Sonicate the centrifuge tube for about 10 seconds using a         sonic dismembrator.     -   4. Transfer the fluid biomedical specimen to a new centrifuge         tube using a micropipette with a 150 microliter tip.     -   5. Vacuum centrifuge the sample until dry. (About 30 min) on low         heat.     -   6. Resuspend the residue of the centrifuge tube with 5         microliters of water.     -   7. Mix the resulting mixture using vortex shaker until the         contents are thoroughly mixed.     -   8. Place 1 microliter of the resulting sample on the slide.     -   9. Vacuum dry the slide in a desiccator for 5 minutes, until all         spots are dry. Store slide in a desiccator when not in use.

Example 8 Classifying a Disease Condition within a Single Gender Population

The following example verifies that acceptable results may be established using only women, comparing healthy women to women with IC. Blood was collected from the finger, using a needle stick, into a capillary tube, which allows less invasive collection and easier storage of samples. Samples were then analyzed from 25 women with IC and 23 healthy using the same IRMS method described above (see Table 5). The 3-D projection shown in FIG. 6(A) and the Coomans plot in FIG. 6(B) demonstrate that a large degree of separation was still possible, and the inter-class distance was measured at 2.7.

TABLE 5 SIMCA prediction models in Capillary Tubes Healthy IC Total Number of specimen 23 25 48 No of spectra 23 25 48(1/specimen) Spectra used for SIMCA models 15 17 32 Spectra used as blinds 8 8 16 Correctly classified Blinds 8 7 15 Mispredicted 0 0 Not classifiable 0 1  1

Example 9 Classifying a Disease Condition from Bloodspot Card Samples

The following example demonstrates the feasibility of using bloodspot cards for sample collection. Blood can be directly collected on the card, and it does not need to be frozen, making this an ideal method since transportation and storage would not be an issue. Samples were analyzed from 14 women with IC and 16 healthy using the same IRMS method described above.

TABLE 6 SIMCA prediction models in Blood Spot Cards Healthy IC Total Number of specimen 16 14 30 No of spectra 16 14 30(1/specimen) Spectra used for SIMCA models 16 14 30 Spectra used as blinds Correctly classified Blinds Mispredicted Not classifiable

Overall, these examples show that IRMS can detect differences between healthy patients and those with IC using multivariate data analysis to create clusters that differentiate the two groups. We have found that this method can reliably classify patients having IC. The examples also establish that such a classification can be established using capillary blood and blood spot cards from a finger-stick, rather than collecting blood from the veins.

A hydrophobic grid was created to aid in concentrating bloodspot card samples, which allows more information to be collected from a single spectrum. Scans were collected by reflectance of IC (n=14) and healthy (n=16) samples. The Coomans plot (FIG. 7A) shows an interclass distance of 3.1, and a 3-dimensional projection (FIG. 7B) in space shows clear separation between the two groups.

Example 10 Classifying Flare v. Remission and Severity of Symptoms in Humans

The course of many diseases is marked by periods of flare-up and remission (e.g., inflammatory bowel disease, interstitial cystitis, etc.). An exemplary embodiment can successfully discriminate between patients experiencing a flare condition from patients experiencing remission from disease symptoms.

IC symptoms (i.e., flare or remission) could be correlated to infrared data from bloodspot cards. O'leary-Sant (OLS) self-reported scores were used to classify women into flare (OLS>15) and remission (OLS<15) categories. A Coomans plot of the two groups (FIG. 8A) shows an interclass distance of 4.5, indicating very good separation between the two groups, and 3-D projection (FIG. 8B) also shows distinct groups. This test will be repeated with a larger sample size.

Once spectral differences between patients in flare and remission were identified, we demonstrated that severity of symptoms, as measured by O'leary-Sant scores, could be linearly correlated with spectral information. Partial least squares regression (PLSR), a multivariate linear regression technique was used to create a predictive model using leave-one-out cross validation. Samples were collected on bloodspot cards and reflectance spectra were collected. A linear relationship was established (FIG. 9) using 8 women with scores ranging from 3-30. The predictive model gave an R² of 0.80 and a standard error of cross validation of 2.9.

The following references and others cited herein but not listed here, to the extent that they provide exemplary procedural and other details supplementary to those set forth herein, are specifically incorporated herein by reference.

-   1. Manu P. 2000. The Pharmacotherapy of Common Functional Syndromes:     evidence-based guidelines for primary care practice. Haworth press.     England -   2. Metts, J. F. 2001. Interstitial Cystitis: Urgency and Frequency     Syndrome. Am Fam Physician 64:1199-1206. -   3. Ratner V., Taylor N., Wein A. J., Hanno P. J. 1999. Epidemiology     of interstitial cystitis: a population based study. J Urol 162:500. -   4. Curhan G. C., Speizer F. E., Hunter D. J., Curhan S. G.,     Stampfer M. J. 1999. Epidemiology of interstitial cystitis: a     population based study. J Urol 161:549-52. -   5. Buffington C A T. 2004. Comorbidity of Interstitial Cystitis with     other Unexplained Clinical Conditions. Journal of Urology.     172:1242-1248. -   6. The Urology Channel:     http://www.urologychannel.com/interstitialcystitis/index.shtml     Accessed on 29 Mar. 29, 2009. -   7. Keay S., Takeda M., Tamaki M. and Hanno P. 2003. Current and     future directions in diagnostic markers in interstitial cystitis.     Intl J Urol 10: S27-S30. -   8. Buffington, C. A. T., Chew, D. J., Woodworth, B. E. 1999. Feline     Interstitial Cystitis. J Am Vet Med Assn, 215:682. -   9. Buffington, C. A. T., Chew, D. J., Woodworth, B. E. 1997.     Idiopathic Cystitis in Cats: an Animal Model of Interstitial     Cystitis. In: Interstitial Cystitis. Edited by G. R. Sant.     Philadelphia: Lippincott-Raven, pp. 25-31. -   10. Clauw D J, Chrousos G P. 1997. Chronic pain and fatigue     syndromes: Overlapping clinical and neuroendocrine features and     potential pathogenic mechanisms. Neuroimmunomodulation. 4 (3):     134-153. -   11. Buffington C A T. 2004. Comorbidity of Interstitial Cystitis     with other Unexplained Clinical Conditions. Journal of Urology.     172:1242-1248. -   12. Nigro D A, Wein A J. Interstitial Cystitis: Clinical and     Endoscopic Features. In: Sant G R, ed. Interstitial Cystitis.     Philadelphia: Lippincott-Raven, 1997:137-142. -   13. Gunnar M R, Fisher P A. Bringing basic research on early     experience and stress neurobiology to bear on preventive     interventions for neglected and maltreated children. Development and     Psychopathology 2006; 18:651-677. -   14. Aaron L A, Burke M M, Buchwald D. Overlapping conditions among     patients with chronic fatigue syndrome, fibromyalgia, and     temporomandibular disorder. Archives of Internal Medicine 2000;     160:221-227. -   15. Aaron L A, Herrell R, Ashton S, Belcourt M, Schmaling K,     Goldberg J, Buchwald D. Comorbid clinical conditions in chronic     fatigue—A co-twin control study. Journal of General Internal     Medicine 2001; 16:24-31. -   16. Clemens J Q, Brown S O, Kozloff L, Calhoun E A. Predictors of     symptom severity in patients with chronic prostatitis and     interstitial cystitis. J Urol 2006; 175:963-6; discussion 967. -   17. Westropp J L, Buffington C A T. 2002. In Vivo Models of     Interstitial Cystitis. The Journal of Urology. 167(2):694-702. -   18. Petrich, W. 2001. Mid-infrared and Raman spectroscopy for     medical diagnostics. Appl. Spectrosc. Rev. 36: 181-237. -   19. Naumann, D. 2001. FT-infrared and FT-Raman spectroscopy in     biomedical research. Applied Spectroscopy Reviews 36: 239-298. -   20. Ellis, D., Harrigan, G. G. & Goodacre, R. 2003. Metabolic     fingerprinting with Fourier transform infrared spectroscopy. In:     Metabolic Profiling: Its Role in Biomarker Discovery and Gene     function Analysis (Harrigan, G. G. & Goodacre, R., eds.). Kluwer     Academic Publishers, Dordrecht. -   21. Fabian H., Ngo Thi N. A., Eiden M., Lasch P., Schmitt J.,     Naumann D. 2006. Diagnosing benign and malignant lesions in breast     tissue sections by using IR-Microspectroscopy. Biochim Biophys Acta     1758:874-882. -   22. Shaw R. A. and Mantsch H. H. 2001. Infrared Spectroscopy in     Clinical and Diagnostic Analysis. In Encyclopedia of Analytical     Chemistry. Edited by Robert A. Meyers. John Wiley & Sons Ltd,     Chichester. -   23. Naumann, D. 2000. FT-Infrared and FT-Raman spectroscopy in     biomedical research. In Infrared and Raman spectroscopy of     biological materials. Edited by Hans-lrich Gremlich and Bing Yang.     Marcel Dekker, New York. -   24. Ellis, D. I. and Goodacre, R. 2006. Metabolic fingerprinting in     disease diagnosis: biomedical applications of infrared and Raman     spectroscopy. The Analyst 131:875-885. -   25. Kvalheim O. M., Karstang T. V. SIMCA—Classification by Means of     Disjoint Cross Validated Principal Components Models. Multivariate     pattern recognition in chemometrics, Edited by R. Brereton.     Elsevier, The Netherlands, 1992. -   26. Tirumalai R, Chan C, Prieto A, Issaq H, Conrads T,     Veenstra R. 2003. Characterization of the Low Molecular Weight Human     Serum Proteome. Molecular & Cellular Proteomics 2.10. -   27. Merrell K, Southwick K, Graves S, Esplin M, Lewis N,     Thulina C. 2004. Analysis of Low-Abundance, Low-Molecular-Weight     Serum Proteins Using Mass Spectrometry. Journal of Biochemical     Techniques, Vol (15), 4. -   28. D. Coomans, I. Braeckaert, M. P. Derde, A. Tassin, D. L.     Massart, S. Wold, 1984. Use of a microcomputer for the definition of     multivariate confidence regions in medical diagnosis based on     clinical laboratory profiles. Comput. Biomed. Res. 17, 1-14. -   29. Lambert J, Shurvell H, Lightner D, Cooks G. 1998. Organic     Structural Spectroscopy. Prentice Hall Upper Saddle River, N.J. -   30. Dantzer R, O'Connor J C, Freund G G, Johnson R W, Kelley K W.     From inflammation to sickness and depression: when the immune system     subjugates the brain. Nat Rev Neurosci 2008; 9:46-56. -   31. Hsieh T, Yu K, Lin S. 2007, Possible application of Raman     microspectroscopy to verify the interstitial cystitis diagnosis     after potassium sensitivity test: phenylalanine or tryptophan as a     biomarker, Disease Marker (23).

Having shown and described embodiments of the invention, those skilled in the art will realize that many variations and modifications may be made to affect the described invention and still be within the scope of the claimed invention. Thus, many of the elements indicated above may be altered or replaced by different elements which will provide the same result and fall within the spirit of the claimed invention. It is the intention, therefore, to limit the invention only as indicated by the scope of the claims. 

1. A method for rapidly diagnosing a disease condition of a subject, comprising: obtaining a sample from the subject; separating the sample to obtain a desired fraction; depositing an aliquot of the desired fraction onto a slide; drying the aliquot; collecting infrared spectral data from the aliquot; and categorizing the subject's condition by analyzing the infrared spectral data using multivariate classification models.
 2. The method of claim 1, wherein the obtaining step comprises the substeps: providing a sample collection card; depositing the sample onto the collection card; and resuspending the sample in a fluid medium.
 3. The method of claim 1, wherein the step of categorizing the subject's condition results in classifying the disease condition as in flare.
 4. The method of claim 1, wherein the step of categorizing the subject's condition results in classifying the disease condition as in remission.
 5. The method of claim 1, wherein the step of categorizing the subject's condition results in classifying the disease condition by stage.
 6. The method of claim 2, further comprising the step of: developing multivariate classification models using previously provided spectra corresponding to reference films of afflicted subjects and non-afflicted subjects.
 7. The method of claim 2, wherein: the drying step results in a dried film on the slide; and the collecting step comprises the step of obtaining infrared spectral data from at least two regions of the dried film.
 8. The method of claim 7, wherein: the collecting step comprises obtaining infrared spectral data from at least three regions of the dried film.
 9. The method of claim 2, further comprising the step of: diluting the sample to form a sample-water solution prior to the depositing step.
 10. The method of claim 9, wherein: the sample comprises a fluid biomedical specimen from the subject and the sample-water solution contains at least one part water for each part fluid specimen.
 11. The method of claim 2, wherein: the separating step includes the step of filtering the sample with a membrane.
 12. The method of claim 2, wherein: the separating step includes the step of ultrafiltering the sample with a molecular weight cut-off membrane of a predetermined stringency.
 13. The method of claim 2, wherein: the separating step includes the step of centrifuging the sample using a centrifugal filter device.
 14. The method of claim 13, wherein: the drying step results in a dried film on the slide; and the spectra collection regions are substantially equidistant from the center of the dried film.
 15. The method of claim 2, wherein: the infrared spectral data in the collecting step are obtained via an attenuated total reflectance FT-IR method.
 16. The method of claim 2, wherein the spectral data includes information for wavelengths between 1300 and 1800 cm⁻¹.
 17. The method of claim 2, wherein: the aliquot comprises about 0.01 μl to 1 μl of the desired fraction.
 18. The method of claim 2, wherein: the drying step comprises the application of a vacuum.
 19. The method of claim 2 for the rapid diagnosis of interstitial cystitis. 